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Data Aspirants, Consider These 4 Career Options & Jazz-up Number Games!

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Data Aspirants, Consider These 4 Career Options & Jazz-up Number Games!

Is crunching numbers your favorite hobby?

Are you interested in deciphering how many people use smartphones, regularly?

Do you feel fascinated by the way businesses use data to frame decisions?

If yes, then you are at the right place – a career, where you could leverage this inquisitiveness and knack for numbers is just carved for you. Not necessarily it has to be data science career option, but we’ve charted down top 5 career choices for the data curious you!

Data Scientist

Tagged as the sexiest job of 21st century, data scientist jobs are irresistible. First of all, the field of data science is expanding steadfastly – IBM prediction says the demand for data scientists will increase by 28% by the end of 2020. This brings good news for job seekers, who are on toes to enter the fascinating world of data science, where the salaries are pumping up – already they have touched six figures.

The main objective of data scientists is to collect meaningful data to help businesses formulate strategic decisions. Cleaning up and structuring the data is of primary importance – followed by cutting edge tool implementation, such as algorithms, statistical models and deep learning structures – all of them aids in extracting insights out of relevant data.

Statistician

Other than data geeks, very few love the very idea of becoming a statistician. But for guys who love churning data, the role of statistician is the most fascinating in the world. They help solve the toughest problem with data, while finding and providing answers to crucial questions.

Statisticians’ aptitude for numbers knows no bounds – and the range of projects on which they work is diverse. From ascertaining unemployment rates to nabbing the discerning the effectiveness of prescription drugs to calculating the number of endangered animals living in a given area – from designing the strategies for data collection to nabbing the latest trends, statisticians need to juggle between a lot of tasks, and solve crucial problems.

Computer Scientist

The computers are lifeline of today’s businesses – so jobs related to computing power is selling like hot cakes. The field of computer science is encompassing – nerds in love with data can discover a treasure trove of career options under this umbrella term. If you are a true blue crime buff, choose computer forensics as your leading career option. Or else, are you a major computer game aficionado? Then aspire to become a game developer or architect.

 Today, software developers and architects are witnessing surging demand, and most of the jobs in this technology domain help draw salaries over $100000 annually. So, what you waiting for?!

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Database Administrator

Data is next to oil; of late, it’s been treated as a valuable resource. Thus, we should look for ways to keep it safe and well-protected. Database administrators are ideal for this defensive job. They not only toil to set up fortified databases but also are responsible for maintenance, model up-keeping and implementing security measures. Undeniably, it’s one of the most challenging jobs in the world of data but at the same time, it’s also the most rewarding one – at present, it ranks as the world’s #7 best technology job, according to a notable US tabloid.

Done reading? Now, data-lovers, when are you taking the next step to turn your avocation into your vocation? Pretty soon, right!

Quick Note: DexLab Analytics is offering state of the art Data Science Courses at affordable prices. For more details on Data Science Certification, visit the official page today.

 

The blog has been sourced from – dataconomy.com/2018/06/five-careers-to-consider-for-data-enthusiasts

 

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Estimator Procedure under Simple Random Sampling: EXPLAINED

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Estimator Procedure under Simple Random Sampling: EXPLAINED

In continuation with the previous introductory blog on sampling: An ABC Guide to Sampling Theory, we will take a closer look into the concept of the estimator procedure under Simple Random Sampling with the help of mathematical examples. It will help us understand the underlying phenomenon, the manner to be precise in which the estimator function of sampling works.

Simple random sampling (SRS) is a method of selecting a sample comprising ‘n’ number of sampling units out of the population of ‘N’ number of sampling units such that every sampling unit has an equal chance of being chosen.

The Estimator Procedure under Simple Random Sampling

The process of selection of a sample under SRS (Simple Random Sampling) is random. This means, each number of the population has an equal probability of getting selected, which makes each of the observation identical and independently distributed.

The statistic chosen by the investigation of estimation of random samples need to satisfy a set of certain properties given below:

  1. Unbiasedness
  2. Consistency
  3. Sufficiency
  4. Efficiency

As a matter of fact, investigation is always about coming up with an idea regarding the population parameters based on the sample observations. The best part would be to formulate an unbiased, consistent estimator, which is also efficient. Normally, a sample mean for a set of sample observations is considered to be a very desirable estimator to form ideas about population parameters.

In detail, let’s examine the relevance of each of the properties of an estimator:

Unbiasedness of an estimator

Take a look at the below examples to understand the very idea of unbiasedness.

Example 1:

Answer:-

According to the problem, we have

Adding (1) & (2), we get,

So, from (3), we get:-

 is called an unbiased estimators for .

Now, subtracting (2) & (1), we get –

Example 2:

Assume that an investigator draws a sample from this population using SRSWR. Then show that the sample mean is an unbiased estimator for the population mean.

Now, by specification we have:-

We are redefined to show that:-

L.H.S  :

DexLab Analytics Presents #BigDataIngestion

DexLab Analytics Presents #BigDataIngestion

 

Data sampling is the key to business analytics and data science. On that note, DexLab Analytics offers state of the art Data Science Certification for all data enthusiasts. Recently, they have organized a new admission drive #BigDataIngestion offering exclusive 10% off on in-demand courses, including big data, machine learning and data science courses.

 

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Enjoy 10% Discount, As DexLab Analytics Launches #BigDataIngestion

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Enjoy 10% Discount, As DexLab Analytics Launches #BigDataIngestion

This summer, DexLab Analytics, a pioneering analytics training institute in Delhi is back in action with a whole new admission drive for prospective students: #BigDataIngestion with exclusive discount deals on offer. With an aim to promote an intensive data culture, we have launched Summer Industrial Training on Big Data Hadoop/Data Science. An exclusive 10% discount is on offer for all interested candidates. And, the main focus of the admission drive is on Hadoop, Data Science, Machine Learning and Business Analytics certification.

Data analytics is deemed to be the sexiest job of the 21st century; it’s comes as no surprise that young aspirants are more than eager to grasp the in-demand skills. Especially for them and others, DexLab Analytics emerges as a saving grace. Our state of the art certification training is completely in sync with the vision of providing top-of-the-line quality analytics coaching through fine approaches and student-friendly curriculum.

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That being said, #BigDataIngestion is one of its kinds; while Hadoop and Data Science modules are targeted towards B. Tech and B.E students, Data Science and Business Analytics modules are exclusively oriented for Eco, Statistics and Mathematics students. The comprehensive certification courses help students embark on a wishful journey across various big data domains and architectures, triggering high-end IT jobs, but to avail the high-flying discount offer, the students need to present a valid ID card, while enrolling for the courses.

We are glad to announce that already the institute has gathered a good reputation through its cutting edge, open-to-all demo sessions. The demo sessions has helped countless prospective students in understanding the quality of courses and the way they are being imparted. Now, the new offer announced by the team is like an icing on the cake – 10% discount on in-demand big data courses sounds too alluring! And the admission procedure is also as easy as pie; you can either drop by the institute in person, or else can opt for online registration.

In this context, the spokesperson of DexLab Analytics stated, “We are glad to play an active role in the process of development and condoning of data analytics skills amongst the data-friendly students’ community of the country. We go beyond traditional classroom training and provide hands-on industrial training that will enable you to approach your career with confidence”. He further added, “We’ve always been more than overwhelmed to contribute towards the betterment of skilled human resources of the nation, and #BigDataIngestion is no different. It’s a summer industrial training program to equip students with formidable data skills for a brighter future ahead.”

For more information or to register online, click here: DexLab Analytics Presents #BigDataIngestion

#BigDataIngestion: DexLab Analytics Offers Exclusive 10% Discount for Students This Summer

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An ABC Guide to Sampling Theory

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An ABC Guide to Sampling Theory

Sampling theory is a study involving collection, analysis and interpretation of data accumulated from random samples of a population. It’s a separate branch of statistics that observes the relationship existing between a population and samples drawn from the population.

In simple terms, sampling means the procedure of drawing a sample out of a population. It aids us to draw a conclusion about the characteristics of the population after carefully studying only the objects present in the sample.

Here we’ve whisked out a few sampling-related terms and their definitions that would help you understand the nuanced notion of sampling better. Let’s have a look:

Sample – It’s the finite representative subset of a population. It’s chosen from a population with an aim to scrutiny its properties and principles.

Population – When a statistical investigation focuses on the study of numerous characteristics involving items on individuals associated with a particular group, this group under study is known as the population or the universe. A group containing a finite number of objects is known as finite population, while a group with infinite or large number of objects is called infinite population.

Population parameter – It’s an obscure numerical factor of the population. It’s no brainer that the primary objective of a survey is to find the values of different measures of population distribution; and the parameters are nothing but a functional variant inclusive of all population units.

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Estimator – Calculated based on sample values, an estimator is a functional measure.

Sampling fluctuation of an estimator – When you draw a particular sample from a given population, it contains different set of population members. As a result, the value of the estimator varies from one sample to another. This difference in values of the estimator is known as the sampling fluctuations of an estimator.

Next, we would like to discuss about the types of sampling:

There are mainly two types of random sampling, and they are as follows:

Simple Random Sampling with Replacement

In the first case, the ‘n’ units of the sample are drawn from the population in such a way that at each drawing, each of the ‘n’ numbers of the population gets the same probability 1⁄N of being selected. Hence, this methods is called the simple random sampling with replacement, clearly, the same unit of population may occur more than once inj a simple. Hence, there are N^n samples, regard being to the orders in which ‘n’ sample unit occur and each such sample has the probability 1/N^n .

Simple Random Sampling Without Replacement

In the second case each of the ‘n’ members of the sample are drawn one by one but the members once drawn are not returned back to the population and at each stage remaining amount of the population is given the same probability of being includes in the sample. This method of drawing the sample is called SRSWOR therefore under SRSWOR at any r^th number of draw there remains (N-r+1) units. And each unit has the probability of 1/((N-r+1) ) of being drawn.

Remember, if we take ‘n’ individuals at once from a given population giving equal probability to each of the observations, then the total number of possible example in (_n^N)C i.e.., combination of ‘n’ members out of ‘N’ numbers of the population will from the total no. of possible sample in SRSWOR.

The world of statistics is huge and intensively challenging. And so is sampling theory.

But, fret now. Our data science courses in Noida will help you understand the nuances of this branch of statistics. For more, visit our official site.  

P.S: This is our first blog of the series ‘sampling theory’. The rest will follow soon. Stay tuned.

 

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How Blockchain Technology is Transforming these Four Popular Industries

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How Blockchain Technology is Transforming these Four Popular Industries

Blockchain technology is the next big thing. It is defying industry norms and altering the manner in which industries implement new projects. The decentralized nature of blockchain technology is the key to its success. Blockchain is transforming every organization through its secure and decentralized protocols, protected peer-to peer applications, and a new approach towards distributed management.

Here are some everyday industries that blockchain technology is revamping.

  • Finance:

There are all kinds of opinions regarding how cryptocurrency is impacting macroeconomics pertaining to the financial sector. The rapidly increasing demand for Bitcoin signals a flourishing future for cryptocurrency. In 2017, ICOs (Initial Coin Offerings), which are means of crowd funding centered on cryptocurrency, raised more money than venture capital investments. Cryptocurrencies, like Bitcoin, Ethereum and Ripple are improving their speed for processing transaction fees, and will be able to contend with speed of transaction for credit card companies in the near future. Bitcoin permits people to transfer money across borders instantaneously and at low costs. Many banks, such as Barclays, are set to use blockchain technology to facilitate speedier business procedures.

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  • Cloud Computing:

The evolution of cloud has outmoded hard drives, which was the popular choice for transferring files from one computer to another, even a few years ago. Blockchain-based companies, like Akash, want to seize this opportunity and create an open market place where cloud computing costs are determined by demand and supply, instead of centralized, fixed prices. Most large-scale data centers depend on idle computing power. Akash Network makes idle server capacity available for cloud deployments. This system enables users to ‘’rent’’ idle computing power and providers to generate revenue from their idle power. Developers specify their deployment conditions in a file that is posted on the Akash blockchain. Providers capable of fulfilling these conditions bid on it. Low bid wins; after this parties go off chain to allocate workload in Docker containers. Akash tokens are then transferred from tenant‘s wallet to provider’s wallet.

  • Online Gaming:

The online sports industry is embracing the blockchain technology. An increasing number of developers in the world of e-Sports are employing blockchain technology and cryptocurrencies. Leading fantasy sport companies, like MyDFS, permit their users to create virtual arrays of real players and obtain winnings through tokens. In-app purchase is the newest monetization model for Smartphone app games. Blockchain technology is also advantageous for e-Sports betting platforms. The tech constructs a secure environment for low fee betting that is free from the control of a central party.

  • Decentralized Governance:

One of the most famed features of blockchain is decentralization. The thought of decentralized, autonomous organizations is no doubt very fascinating, but they are very difficult to establish. A hierarchical structure, where one person or group tends to dominate, is very natural. However, new and advanced frameworks are facilitating decentralized platforms to function effectively. An example of such a framework is DAOstack, which is striving to build a platform that enables collectives to self-organize around similar goals and interests. It is a platform that authorizes emerging organizations to select suitable governance model that will work for them and execute the same through DAOstack’s technological protocol. DAOstack’s founding principle is collaboration- it aims to provide a setting where goals of individuals can work in harmony with goals of a group.

The ‘’blockchain boom’’ is driving breakthroughs for a range of industries. This is just the beginning, though. As this tech evolves, it will enable rapid progress across every industry.

To read more blogs on emerging technologies, follow DexLab Analytics; it is a premier institute providing data science certification courses in Delhi. Do take a look their data analytics certification courses.

 

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5 Best Data Science Resources to Ace the Game of Data

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Wondering how a data scientist makes advances in his data career? Or how does he expand his skills in the future? Reading is the most common answer; nothing helps better than keeping a close eye on the industry news. Data science is evolving at a rapid speed; to be updated with the latest innovations and technology discoveries would be the best thing to stay ahead of the curve.

5 Best Data Science Resources to Ace the Game of Data

If you are a newbie in this field, make sure you are well-read about the current industry trends and articulate it well to the HR heads that you are someone who is always a step ahead to consume knowledge about data science and its related fields. This helps!

A wide number of data science blogs and articles are available over the internet, but with so many options, it’s easy to feel lost. For this and more, we have compiled a comprehensive list of 5 best data science blog recommendations that would help aspiring data scientists maneuver smoothly through this sphere.

Data Elixir

For a one stop destination for all things DATA, Data Elixir is the right choice. Crafted by ex-NASA data scientist Lon Riesberg, Data Elixir offers a list-wise view of the posts; easy categorization of content is anytime preferable and renders easy search options.

Data Science Weekly

The brain child of Hannah Brooks and Sebastian Gutierrez, Data Science Weekly is the ultimate hub for recent news, well-curated articles and promising jobs related to data science. You can either sign up for their newsletter or simply scroll through their archives dated back to 2013.

The Analytics Dispatch

The Analytics Dispatch is more like a newsletter content creating hub, wherein they send weekly emails about data science related stuff to its readers. Collected, analyzed and developed by a robust team at Mode Analytics, which also happens to be an Udacity partner, the newsletters focus on practical advices on data analysis and how data scientists should work.

Let’s Take Your Data Dreams to the Next Level

O’Reilly Media’s data science blog

To read some of the most amazing articles on AI and data science, make O’Reilly Media’s data science blog your best companion. The articles are curated, researched and written by influencers and data science pundits, who are technically sound and understands the advanced nuances of the field in-depth.

Cloudera

Being top notch big data software, Cloudera’s contribution to the world of data science is immense. Time to time, it publishes interesting articles, know-hows and guides on a plethora of open source big data software, like Hadoop, Flume, Apache, Kafka, Zookeeper and more.

Besides, DexLab Analytics, a pioneering analytics training institute headquartered in Gurgaon, India also publishes technical articles, amazing blogs, riveting case studies and interviews with analytics leaders on myriad data science topics, including Apache Spark, Retail Analytics and Risk Modeling. The content is crisp, easy to understand and offers crucial insights on a gamut of topics: it helps the aspiring readers to broaden their horizons.

The realms of data science are fascinating and intimidating as well; but with the right knowledge partner, carry suave data skill in your sleeves – Data Science Courses in Noida from DexLab Analytics are the best in town! Also, their Business Analytics Training Courses in Noida are worth checking for.

Some of the parts of the blog have been sourced from – http://dataconomy.com/2018/01/5-awesome-data-science-subscriptions-keep-informed/ and https://www.springboard.com/blog/data-science-blogs

 

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Facial Recognition Technology: Where Opportunities are Endless and Science is Terrific

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Facial Recognition Technology: Where Opportunities are Endless and Science is Terrific

We are on the verge of the Fourth Industrial Revolution – where massive amounts of texts, tweets, photos, videos, status updates, GPS coordinates, reposts and clickstreams are being pumped out into the digital universe. This data is like the food for colossal artificial intelligence.

If we talk about resources, the ocean that AI-induced data has filled up is nothing if compared to California gold rush, Texas Oil boom or similar events. Huge amounts of data are clogging the digital space all over. Algorithms, based on AI are driving innovation in every field of work, right from products to services, and the more data you possess, the more accurate the algorithm is expected to be. As a result, collection and analysis of big data have become a prime focus of companies, big and small.

Introducing Deep Learning

But how does this mammoth AI works? How does it digest this amount of data? Of course through interconnected, high-end devices powered by embedding “eyes”, named as Deep Learning. These artificial neural networks work on the principle of machine learning algorithms and simulate the complex structure of human brains. Employing mammoth data pools and lakes, deep learning determines and interprets intricate patterns, just the way humans do. In fact, some of the artificial neural networks are so adept at incorporating these patterns that they can even mimic the manner in which humans recognize faces.

DeepFace:  A Stiff Competitor of Human Brain

In terms of facial data, Facebook is the largest reservoir of facial data, and back in 2015, it came out with a cutting edge version of “tag photos” feature, DeepFace – it features a nine layer neural network that resembles characteristics in individual photographs with 97.25% accuracy. This fabulous technology not only connects your name with your face, but it can easily pick you out of a crowd, and the figure says a human brain is only 0.28% more effective than DeepFace.

Of late, Facebook has acquired a new patent, “Techniques for emotion detection and content delivery,” – it helps in capturing user’s facial expressions through the camera in real time while they scroll across their feed, recording their emotions for various content. This new-age technology can not only customize your Facebook feed, but can also link numerous live in-store cameras for a better shopping experience, piling up data from Facebook and determining the shopper’s present mood and preference.

Facebook and Beyond

Though Facebook is dominating the waters of facial recognition, there are several other companies that are trying their luck into this domain. Ebookers, a sub-site of Expedia has launched a tool named SenseSational, which employs real time facial recognition software to monitor users’ faces, while they peruse over images and sounds that appeal to the senses.

On the other hand, Singapore Technologies Electronics is using facial recognition technology to identify the faces of commuters, as they walk across fare gates and charges their prepaid account respectively. No longer the commuters have to show their fare card while standing in queue; thus it eases the crowd buildup during rush business hours.

In conclusion, companies can anytime look up to deep learning from any angle. The giant of artificial intelligence is forever hungry, you can feed it with data whenever you like, and see it expand and flourish.

Seeking an excellent data analyst training institute in Gurgaon? Look no further; DexLab Analytics is here. With a wide set of comprehensive Data Science Courses in Delhi, this institute is here to satisfy every data need.

Let’s Take Your Data Dreams to the Next Level

The original article first appeared on – https://www.entrepreneur.com/article/311228

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Reigning the Markets: 4 Most Influential Analytics Leaders of 2018

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Data analytics in India is grabbing attention. Data and analytics, together, they play a key role in delivering business opinions, which are high-yielding and relatively new. At the helm of such robust data analytics growth are leaders from numerous organizations who introspect into data to conjure up decisions as a seamlessly as possible. They are masterminds in the world of data analytics.

Reigning the Markets: 4 Most Influential Analytics Leaders of 2018

Here, we will talk about 4 most influential analytics leaders who acted as pioneers of bringing in newer technologies and life-changing innovations into the field of analytics, machine learning, artificial intelligence and big data across diverse domains.

Debashish Banerjee, Managing Director, Deloitte Analytics

With 17 years and more experience in predictive modeling, data analytics and data science, Mr. Banerjee’s extensive contribution in the fields of actuarial risk, data mining, advanced analytics and predictive modeling in particular is phenomenal. He started his career with GE, and initiated and headed insurance analytics, pricing and reserving team of GE, India – one of the firsts in India.

In 2005, he shifted to Deloitte with a mission to initiate the advanced analytics and modeling practice in India, through which he manages and offers leadership support to the Deloitte Consulting’s Data Science practices that stresses on AI, predictive modeling, big data and cognitive intelligence. He mostly worked in marketing, customer and HR domains.

Let’s Take Your Data Dreams to the Next Level

Kaushik Mitra, Chief Data Officer and Head of Big Data & Digital Analytics, AXA Business Services (ABS)

Experienced for over 25 years in integrating analytics, technology and marketing worldwide, Kaushik Mitra dons a lot many hats. Besides assuming leadership roles for diverse domains, like AI, analytics, data science, business intelligence and modeling, Mr. Mitra is at present involved in driving an array of data innovation coupled with technology restructuring in the enterprise, as well as coordinating GDPR implementation in ABS.

Before joining ABS, he worked with Fidelity Investments in Bangalore, where he played a pivotal role in establishing their data science practice. Armed with a doctorate in Marketing from the US, he is a notable figure in the world of analytics and marketing, along with being a frequent speaker in Indian industry networks, like NASSCOM and other business forums.

Ravi Vijayaraghavan, Vice President, Flipkart

Currently, Ravi Vijayaraghavan and his team are working on how to leverage analytics, data and science to improve decision-making capabilities and influence businesses across diverse areas within Flipkart. Before joining Flipkart, he used to work as Chief Data Scientist and Global Head of the Analytics and Data Sciences Organization at [24]7.ai. It was here that he created, developed, implemented and optimized machine learning and analytics driven solutions. Also, he held important leadership portfolios at Mu Sigma and Ford Motor Company.

Deep Thomas, Chief Data & Analytics Officer, Aditya Birla Group

“Delivering nothing but sustained and rising profitability figures through potent digital transformation and leveraging data, business analytics, multi-disciplinary talent pool and innovative processes” – has been the work mantra of Deep for more than two decades. Being the Chief Data & Analytics Officer for Aditya Birla Group, he spearheads top of the line analytics solutions and frames organization-wide initiatives and tech-induced programs to enhance business growth, efficiencies and productivity within an organization.

Initially, he headed Tata Insights and Quants, the much acclaimed Tata Group’s Big Data and Decision Science Company. Apart from this, he held a variety of leadership positions in MNCs like Citigroup, HSBC and American Express across US and India to boost global digital and business transformation.

This article has been sourced from – https://analyticsindiamag.com/10-most-influential-analytics-leaders-in-india-2018

For more such interesting blogs and updates, follow DexLab Analytics. It’s a premier data science certification institute in Delhi catering to data aspirants. Take a look at their data science courses in Delhi: they are program-centric and nicely curated.

 

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New Intelligence is being added to Massive Storage Management System

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Pioneers of High Performance Storage System (HPSS) are devising ways to streamline and rationalize data management products for its upcoming eighth generation. 25 years back, US Department of Energy research laboratories and IBM together built HPSS to support massive government science research projects. Why? The Hierarchical storage solution is undeniably a rewarding concept which uses organization policies and software automatic tricks to decide which data to save, the location where it should be saved, the best time to move it to different storage devices and when to delete it.

New Intelligence is being added to Massive Storage Management System

“How do you know what you’re archiving? We’re talking about archives now that are hundreds of petabytes to an exabyte. We think we’re going to be there in 2-3 years,” asked Todd Herr, a storage architect for supercomputing from Lawrence Livermore National Laboratory, CA.

The HPSS website catalogues 37 publicly disclosed customers, while other customers are kept discreet. At present, version 7.5.1 from last year is on the run, but version 7.5.2 might be hit, while the next year will see 7.5.3, as given in the online roadmap.

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However, version 8 is not yet available on the official roadmap, but here’s what the insiders have to say about it…

“What I think our challenge is, is to become good data curators. And I think that’s where we’re going to point the product,” Herr shared. This will turn HPSS become more capable for data mining and assign metadata to itself.

In order to do that, the first thing to be done is to reveal information in the archive about a few overarching namespace applications. Herr explained, “Right now we are working on that (referring to software made by companies such as Atempo, Robinhood, Starfish, and StrongLink). I think the next step there is scaling out metadata performance, such as database partitioning and virtualizing multiple processors when performing searches.”

Another important part of HPSS is related to the software that works with tape storage – “What we’re trying to do is enable fast access to tape. If you look across the industry spectrum, the words fast and tape generally don’t go together,” Herr intimidated. The scientists at Livermore are capable of accessing research data on tape, even that existed more than 50 years ago.

Speed-matching buffers can save the day – when placed between primary disk storage and archive tape storage, they can be used to both read and write. Some other physical improvements include faster head placements and tape motors.

“We’re going to hit a problem way faster than most sites, and certainly faster than the vendors themselves because they cannot replicate our environment in most testing,” Herr asserted.

Herr’s employer’s next supercomputer, Sierra is going to operate at up to 125 petaflops and will have a 125-petabyte file system for performing ample tests to find new ways of speeding up performance and administer advanced data storage mechanisms.

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The article has been sourced from – https://www.techrepublic.com/article/fed-and-ibm-researchers-adding-new-intelligence-to-massive-storage-management-system

For more such interesting ideas and discussions, stay tuned to DexLab Analytics. It is a premier analytics training institute headquartered in Delhi, NCR. Their data science certification courses are excellent.

 

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